Multi-scale Feature Fusion:Deep arbitrary-shape scene text detectors usually involve fusing multi-scale features generated by the backbone network, before feeding into the prediction head.This fusion between low-level and high-level features could enrich the feature representations, facilitating the model to detect texts with various scales.However,existing methods usually introduce different fusion techniques,which further make trouble for fairly investigating the representations of describing texts. These different fusion techniques not only influence the performance,but also result in differnt memeory and speed.Besides, even though the module adopts similar fusion strategies, it could still bring different performance gains, due to different resolutions of the output maps. For example, in PSENet [1]}, when the resolution of the output map is 1/4 of the input image (termed as `PSENet-4s'), it decreases by 2.3% and 1.3% in \(\textit {F-measure}\)  for the datasets CTW and TOT respectively, compared with `PSENet-1s'.
